More Like This

Preview

Some of the major concepts of validity and bias in epidemiological research are outlined in this chapter. The contents are organized in four main sections: Validity in statistical interpretation, validity in prediction problems, validity in causal inference, and special validity problems in case–control and retrospective cohort studies. Familiarity with the basics of epidemiological study design and a number of terms of epidemiological theory, among them risk, competing risk, average risk, population at risk, and rate, is assumed. A number of textbooks provide more background and depth than...

Some of the major concepts of validity and bias in epidemiological research are outlined in this chapter. The contents are organized in four main sections: Validity in statistical interpretation, validity in prediction problems, validity in causal inference, and special validity problems in case–control and retrospective cohort studies. Familiarity with the basics of epidemiological study design and a number of terms of epidemiological theory, among them risk, competing risk, average risk, population at risk, and rate, is assumed. A number of textbooks provide more background and depth than can be given here; see, for example, Kelsey et al. (1996), Rothman and Greenland (1998), Koepsell and Weiss (2003), and Checkoway et al. (2004).

Despite similarities, there is considerable diversity and conflict among the classification schemes and terminologies employed in various textbooks. This diversity reflects that there is no unique way of classifying validity conditions, biases, and errors. It follows that the classification schemes employed here and elsewhere should not be regarded as anything more than convenient frameworks for organizing discussions of validity and bias in epidemiological inference. Many types of bias can be qualitatively illustrated with causal diagrams, which reveal the relationships among confounding and selection bias; for example, see Greenland et al. (1999), Pearl (2000), Cole and Hernán (2002), Hernán et al. (2004), and Glymour and Greenland (2008).

Several important study designs, including randomized trials, prevalence (cross-sectional) studies, and ecological studies, are not discussed in this chapter. Such studies require consideration of the validity conditions mentioned earlier and also require special considerations of their own. Further details of these and other designs can be found in the general textbooks cited in the preceding paragraphs. For discussions of the problems of ecological studies, see Greenland (2001, 2002, 2004a). Meta-analytic methods are discussed by Greenland and O’Rourke (2008). A number of central problems of epidemiological inference are also not covered, including choice of effect measures, problems of induction, and causal modelling. For critical discussions of effect measures by the present author, see Greenland (1987, 1999, 2002b), Greenland and Robins (1988), Greenland et al. (1986, 1991), Rothman and Greenland (1998, Chapter 4), and Greenland et al. (1999). Greenland (1998a, b) discusses problems of inductive and probabilistic inference, Greenland and Brumback (2002) review causal modelling in epidemiology, and Rothman et al. (2008) discuss broader issues in causal inference and philosophy.

Among the deeper problems not discussed here are the failure of conventional statistical methods to account for non-random sources of uncertainty, and the tendency of people (including scientists) to make overconfident and biased inferences in the face of uncertainty (Gilovich et al. 2002). Analytical approaches to these problems are discussed by Eddy et al. (1992) and Greenland (2005).